Proteolytic enzymes play essential roles for all living organisms, from bacteria to higher eukaryotes. Understanding the function of the large numbers of proteases that have been uncovered by whole genome sequencing is a significant challenge, and novel methods are needed to rapidly provide functional information on a proteome-wide scale. Peptide libraries have been used over the last 15 years to determine cleavage site sequence motifs, which can be used to design model substrates, identify in vivo protein substrates, and generate potent and specific protease inhibitors. Recent advances in peptide microarray technology now allow higher throughput analysis of peptide cleavage specificity, but current approaches can only be applied to a subset of proteases and provides limited information. We propose to develop a novel platform that combines peptide microarrays with mass spectrometry to allow for rapid, general, and thorough analysis of protease cleavage selectivity. Dually-labeled fluorescence resonance energy transfer peptide substrates will be immobilized on modified glass slides at high density via their amino termini. Treatment of slides with a protease of interest results in site- specific cleavage of a subset of peptides. The resulting change in fluorescence will be detected on a microarray reader, allowing quantitative assessment of the extent of cleavage of each peptide on the array. The specific sites of cleavage within each substrate peptide will be determined by subjecting the released amino terminal fragments to liquid chromatography-electrospray mass spectrometry. Consensus cleavage motifs are subsequently derived from sequence alignments of the cleaved peptides. Because proteases have been implicated widely in human disease, this methodology has the potential to impact research and drug discovery in a number of areas, including cancer, arthritis, autoimmune disease, and infectious disease. Proteases, the enzymes that break down proteins, are essential for normal physiology yet can contribute to human disease in multiple contexts, including cancer, viral infection, autoimmune disease, and Alzheimer's disease. The proposed research will develop new micro-scale technology to rapidly analyze the way that proteases recognize their target proteins. This information will contribute to our understanding of how proteases function and will be applicable to the discovery of new protease inhibitor drugs.